https://github.com/lenguyenthedat/dextra-mindef-2015
My solution for Dextra Data Science Challenge #44 (Singapore Ministry of Defense) https://challenges.dextra.sg/challenge/44
https://github.com/lenguyenthedat/dextra-mindef-2015
classification data-science machine-learning xgboost
Last synced: about 1 year ago
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My solution for Dextra Data Science Challenge #44 (Singapore Ministry of Defense) https://challenges.dextra.sg/challenge/44
- Host: GitHub
- URL: https://github.com/lenguyenthedat/dextra-mindef-2015
- Owner: lenguyenthedat
- Created: 2015-08-21T09:20:43.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2020-04-18T09:38:29.000Z (about 6 years ago)
- Last Synced: 2025-06-08T17:40:31.913Z (about 1 year ago)
- Topics: classification, data-science, machine-learning, xgboost
- Language: Python
- Size: 18.5 MB
- Stars: 8
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Ministry of Defence Data Analytics Challenge
============================================
**Note**: A few people asked me for the challenge's data source. Unfortunately, I am not authorized to publicly release it - if you need it, please do send the request to either Mindef or Dextra.sg instead of sending it to me.
Challenge URL: http://www.dextra.sg/ministry-of-defence-data-analytics-challenge/
Quick analysis with Tableau Public [Removed due to non-disclosure agreement]
Libraries used: Scikit-Learn, Pandas, XGBoost, Mathplotlib
Scores:
-------
[Public Leader Board (5th/158 Participants)](https://challenges.dextra.sg/challenge/44)
- 0.0169515: best Single XGBoost model
- 0.0168939: blending multiple XGBoost models with different Features Set.
[Private Leader Board (1st/158 Participants)](http://www.dextra.sg/mindef-challenge-results/)
- 0.0141351
Submission History (only the best one):
---------------------------------------
Only Native XGBoost was recorded since it just dominated everything.
1) Public Leader Board 0.0171364
$ python classify-xgb-native.py # 990r depth6
0.0155765992602
0.019516592639
0.00988590074655
0.0141124661651
0.014303086534
Mean: 0.014678929069 (Local Score)
2) Public Leader Board 0.0172253
$ python classify-xgb-native.py # 180r
0.0157726389016
0.0201645979107
0.0095532522597
0.013888759618
0.0139117869773
Mean: 0.0146582071335 (Local Score)
3) Public Leader Board 0.0171475
$ python classify-xgb-native.py #added age_gender, rm a bunch of features
0.015551655811
0.019148557532
0.00965389534226
0.0139233429833
0.0139280448029
Mean: 0.0144410992943 (Local Score)
4) Public Leader Board 0.0171112
$ python classify-xgb-native.py # promo - gender
0.0155083548415
0.0189263516813
0.00951782504063
0.0140093232169
0.014178032663
Mean: 0.0144279774887 (Local Score)
5) Public Leader Board 0.0170703
$ python classify-xgb-native.py # cap salary 101%
0.0153414063482
0.0189991328711
0.00959486331913
0.0139794582592
0.0140253377611
Mean: 0.0143880397117 (Local Score)
6) Public Leader Board 0.0170369
$ python classify-xgb-native.py # INJURY TYPE as String
0.0153022751895
0.0189944794534
0.00957494483944
0.0139220394066
0.014069437855
Mean: 0.0143726353488 (Local Score)
7) Public Leader Board 0.0169515
$ python classify-xgb-native.py # better minchildage # treat as str
0.0152455036731
0.0189285563506
0.00961418416464
0.0139189502782
0.0139664367926
Mean: 0.0143347262518 (Local Score)